facial expression recognition by: stephanie tsai nazia hashmi michelle aleong
TRANSCRIPT
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Facial Expression Recognition
By: Stephanie Tsai
Nazia Hashmi
Michelle Aleong
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What is Facial Expression Recognition?
Facial Expression Recognition has been defined as the biometric identification by scanning a person’s face and matching it against a library of faces
Process by which the brain and mind understand and interpret the human face
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Why Facial Expression?
Behavioral assessment of emotion and paralinguistic displays
Facial nerve disorders Computer systems that understand human behavior Speech recognition. Security systems. Lie detection. Video compression in telecommunications. Emotion for animation.
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FACS
Facial Action Coding System Most widely used method for measuring and
describing facial behaviors Explains how to categorize facial behaviors
based on the muscles that produce them Goal is to create a reliable means for skilled
human scorers to determine the category in which to fit each facial behavior.
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Once upon a time…
Developed by Paul Ekman (UCSF) & William Friesen of Langley Porter Neuropsychiatric Institute in San Francisco in 1978
Current computer programs being developed at the University of Pittsburgh and Carnegie Mellon University, the other by a team at the Salk Institute in La Jolla, California
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Action Units DiagramAction Unit Description Facial Muscle Example Image
1 Inner Brow Raiser Frontalis, pars medialis
2 Outer Brow Raiser Frontalis, pars lateralis
3 Brow Lowerer Corrugator supercilii
5 Upper Lid Raiser Levator palpebrae superioris
10 Upper Lip Raiser Levator labii superioris
25 Lips Part Depressor labii inferioris
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How it Works
Action Units (AUs) are the measurement units of FACS
44 AUs FAC coder “dissects” the expression and
decomposes it into the specific AUs that produce the movement
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Scoring
The scores consist of the list of AUs that produce it
Descriptive only AU 1+5+25
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Problems with FACS
Human-observer based methods for measuring facial expression are labor intensive, qualitative, and difficult to standardize.
Less than 100% inter observer reliability
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Superman (aka. the computer) to the rescue!! Goal is to make feasible more rigorous,
quantitative measurement of facial expression in diverse applications
Computers can recognize specific action units
Unbiased based on person's gender, race or age.
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Automated Face Analysis
Automated Face Analysis Training data on group of more than 200 people
of different racial and ethnic backgrounds.“The hardest and most time-consuming part of all
this work is collecting a database of images that is diverse enough and big enough to train the computer," says Sejnowski.
3-generation system developed at CMU
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Generation I
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Generation 2
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Generation 3
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Current Research
Competitions to explore the different methods to analyze the expressions from the same set of videos
Research unit ongoing at CMU Department of Computer Science
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Are you ready to have a computer know what you’re
feeling?